{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customizing Plot Legends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot legends give meaning to a visualization, assigning meaning to the various plot elements.\n",
    "We previously saw how to create a simple legend; here we'll take a look at customizing the placement and aesthetics of the legend in Matplotlib.\n",
    "\n",
    "The simplest legend can be created with the ``plt.legend()`` command, which automatically creates a legend for any labeled plot elements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir(\"/home/lab466/pythons/pyDSHandbook3/notebooks\")\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('classic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zrVg5lQU8/rhmwXnzgEcfDftm7ebNwA036NK733+vSy+QiypW1PKSNm2Apk11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\nlEOSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff683544240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 1000)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, np.sin(x), '-b', label='Sine')\n",
    "ax.plot(x, np.cos(x), '--r', label='Cosine')\n",
    "ax.axis('equal')\n",
    "leg = ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But there are many ways we might want to customize such a legend.\n",
    "For example, we can specify the location and turn off the frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/ztVxh/wWRgW75x59bX341nHsqNQSmVt26dB/u3ZOh+YTEV28s107rdB85x2d\nT0UhICJCG0jz5ukIdhHCwdNQt2YN0KePzsB88cWwzxrZ2cDch1fg6c86YNprBj16OB2R9/btA/r3\n1/kxb7/NVjoFBld3tFGbNtqyjY4O++4KQCeQ9nv7FixcZDBihE7A2L/f6ag8I6I7HDVtqkME69Yx\nqVNoYWIPB9HRuphE6dJOR+KZfCoSWrTQksjYWB3reuml8NgDfP16fa+dNk0HhkePtmK1g6JHRCcG\nLlkShOVJg4+JPdyF4pPS7dadmOvUyXfwt0QJrSBZs0bX+GrSBPj009C8SxkZwGOP6QJeDz2kpZyN\nGzsdFXnNGF3cKC5OuzktWJTv95jYw9nZs0D79trcDYU6cLdbM3SzZsDrr+u+X1WqFPhn9eppeeD4\n8cBTTwEul++LIPnL4cPAyJEaY1SUVr888og1KwgXbbffriWR3boBt96qazZt3+50VGrXLp9aOD49\nPY0x9xpjUo0xbmNMM1/ORV6IiNCkvmqVLpD+3HPAb785E8t332kT9tlndVbO1197tFiMMVr3vXGj\nLoDUq5e+ZyUkOFPOv3u3vslcd52u+LB+vT7U5csHPxYKoKgoLbfdsgWoVk1LJJ2UnKxrYDdv7tPi\n7D5VxRhjYgG4AbwO4B8i8l0+x7IqJpBSU7Uf/pNPgGHDtJkZTPv26cfZLl38MsibnQ188IEWAmVm\navdHnz6BrVrLztZ+85kztdq0Xz9dhKlWrcBdkwgnT+qTffp0IC1N+/weewwoWxaAg6s7GmMSwcQe\nGg4d0n7thg39f24RYNs27TsPUoWOiNa/z5qlS283b64LI3Xtqh9SfHXqlH7gWbRIX1vXXqsbYDz4\nYPiNVVOAiOiOTe3b6xMvOtq/558+XRtkAwfqIM5F6wgwsVP+Jk/WfUMbNNBO49q185/Kv2ePfhLY\nvBlYu1bXkI+M1K8dmPBx6pQOri5erMUMZctqb0+zZrpsQa1a+mk6rwXHRHRDpr179S6tX6+9R0lJ\nWrZ42226ZI0/3izIMjk5OlHhv//VnZvq1NHJgy1bAvffn//fut06ULN1q26J2bmzx5cPSGI3xiwD\nUDmPX40SkYW5xySCiT30rVypuzZt2qR9ijt3ah/j558DrVr98fiePbXPPjZWs2e7dkCNGiFRT+92\n67jXt99qgk5J0fGmjAxN+KVL6056Z85oV86RI5rwq1XTu9O0KXDDDVq6GOZzviiYzpzRVsGaNbqy\n3cSJfzw7tzdqAAAG00lEQVRm2zZt2Z88qU/I0qX1zaBDB92swEMh32IfM2bM+e9dLhdcLpfP1yYf\niOiTr1gxvVkgKws4elQ/mJw6BRQvrh9KypY932VJFFhZWTr6XqoUEBPj8WsrMTERiYmJ578fO3Zs\naCd2ttiJiDwT9CUFjDHdjTH7ALQE8KkxZqkv5yMiIt9xETAiohDGRcCIiIiJnYjINkzsRESWYWIn\nIrIMEzsRkWWY2ImILMPETkRkGSZ2IiLLMLETEVmGiZ2IyDJM7ERElmFiJyKyDBM7EZFlmNiJiCzD\nxE5EZBkmdiIiyzCxExFZhomdiMgyTOxERJZhYicisgwTOxGRZZjYiYgsw8RORGQZJnYiIsswsRMR\nWcanxG6MedEYs8UYs9EY819jTLS/AiMiIu/42mL/AkADEWkEYCuAEb6HREREvvApsYvI5yKSk/vt\nWgDVfA+JiIh84c8+9r8CWOLH8xERkRciCzrAGLMMQOU8fjVKRBbmHjMKQA6AufmdKz4+/vzXLpcL\nLpfLg1CJiOyXmJiIxMREn85hRMS3ExjTD8DfAHQUkVP5HCe+XouIqKgxxkBEjCd/U2CLvYALdgEw\nDED7/JI6EREFj08tdmPMdgDFAfya+6O1IjLwEseyxU5E5CFvWuw+d8UU+kJM7EREHvMmsXPmKRGR\nZZjYiYgsw8RORGQZJnYiIsswsRMRWYaJnYjIMkzsRESWYWInIrIMEzsRkWWY2ImILMPETkRkGSZ2\nIiLLMLETEVmGiZ2IyDJM7ERElmFiJyKyDBM7EZFlmNiJiCzDxE5EZBkmdiIiyzCxExFZhomdiMgy\nTOxERJZhYicisoxPid0Y86wxZqMxJtkY87kxpqq/AiMiIu8YEfH+j40pKyLHcr9+AsD1IjLwEseK\nL9ciIiqKjDEQEePJ3/jUYj+X1HOVAsDMTUTksEhfT2CMmQDgQQBHAXTwOSIiIvJJgV0xxphlACrn\n8atRIrLwd8eNAHC5iIy5xHnYFUNE5CFvumIKbLGLyK2FPNdcAIsB5JnYASA+Pv781y6XCy6Xq5Cn\nJiIqGhITE5GYmOjTOXwdPK0jIttyv34cQHsR+fMljmWLnYjIQwFpsRfgeWNMXQBuALsB5FkRQ0RE\nweNTi92jC7HFTkTksaCXOxIRUehhYicisgwTOxGRZZjYHeBrKZNN+FhcwMfiAj4WvmFidwCftBfw\nsbiAj8UFfCx8w8RORGQZJnYiIssEtY49KBciIrKMp3XsQUvsREQUHOyKISKyDBM7EZFlAp7YjTFd\njDE/GWO2G2OGB/p6ocoYU90Ys9IYs8kYk2qMGeJ0TE4zxkQYY9YbYxKcjsVJxphoY8yHxpgtxpjN\nxpiWTsfkFGPM33NfHynGmHeNMZc7HVMwGWPeNMYcNMak/O5nFYwxXxhjtuX+W76g8wQ0sRtjIgC8\nCuA2ANcD6G2MuT6Q1wxhOQCGisj1AFoAGFSEH4tzhgDY7HQQIeAlAJ+JSD0AjVFEHxNjzFUAngDQ\nTEQaAIgA0MvZqIJuFoAuF/1sOIDlIlIHwPLc7/MV6Bb7TQC2i8jPInIGwHsA7grwNUOSiBwQkR9y\nvz4OffFe5WxUzjHGVANwO4CZTsfiJGNMOQDtALwBACJyRkSOOBuVoyIBlDDGRAIoCSDN4XiCSkRW\nATh80Y/vAjA79+vZAO4u6DyBTuxXAdj7u+/3oQgns3OMMTUBNAWwztlIHDUVwDDoWv5FWS0AGQDe\nyu2WmmmMKeV0UE4Qkf0A/gVgD4ADAI6KyOfORhUSrhSRA7lf/wLgyoL+gIOnQWaMKQ3gIwBxInLM\n6XicYIzpBuCgiHzvdCwhIBLADQBeE5GmAE6iEB+1bZTbd3wX9M2uKoBSxpg+zkYVWnI3tSiwRj3Q\niX0/gOq/+75a7s+KJGNMFDSpzxWRj52Ox0GtAdxpjNkF7Z67xRjzjrMhOWYfgH0icu7T24fQRF8U\n3Qpgp4hkiEg2gI8BtHI4plCQboypAgC5/x4s6A8CndiTANQxxtQyxhSDDoQsCvA1Q5IxxkD7UTeL\nyBSn43GSiIwQkWoiUhP6nFghIkWyZSYivwDYm7vFJAB0BLDJwZCctAdAC2NMydzXS0cU0YHkiywC\n0Df3674AFhb0B77ueZovEckxxgwGsBQ6wv2miKQG8pohrDWABwD8aIxJzv3ZSBFZ7GBMFBoeBzA3\nt/HzM4CHHI7HESKyzhjzIYAfoFVk6wHMcDaq4DLGvAvABaCiMWYfgDEAngcw3xjzMHRv6Z4FnodL\nChAR2YWDp0RElmFiJyKyDBM7EZFlmNiJiCzDxE5EZBkmdiIiyzCxExFZhomdiMgy/w8EBAeya42r\nUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff683544240>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.legend(loc='upper left', frameon=False)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the ``ncol`` command to specify the number of columns in the legend:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7jOcjIqIgJJ7qABH5HEDVAn71uKpOzz/mcQB5AKYUdq5hw4Yd/drn88Hn8wUQ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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff683544240>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.legend(frameon=False, loc='lower center', ncol=2)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use a rounded box (``fancybox``) or add a shadow, change the transparency (alpha value) of the frame, or change the padding around the text:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AIUNs8e3NN3O6Y/TZuNHmud13n71dRzlV4NlnbSrk++8DbdsGeTIPvGCzsoB7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\nREQew8ROROQx/w+HKlCTSfbFpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff683544240>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.legend(fancybox=True, framealpha=1, shadow=True, borderpad=1)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more information on available legend options, see the ``plt.legend`` docstring."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choosing Elements for the Legend\n",
    "\n",
    "As we have already seen, the legend includes all labeled elements by default.\n",
    "If this is not what is desired, we can fine-tune which elements and labels appear in the legend by using the objects returned by plot commands.\n",
    "The ``plt.plot()`` command is able to create multiple lines at once, and returns a list of created line instances.\n",
    "Passing any of these to ``plt.legend()`` will tell it which to identify, along with the labels we'd like to specify:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9HQCXDVyGdRfXodGg7Hjyz+2w+gGWI/XTn6pg41enA/bsAebN6/DQqcHBuN7U\nhJz6egkEc55VZ1bh4X4Pw9fDjo5qKnD9EDEj6tFHOz62e2B3DI4YLL5QLnhzTyv/VavYDWtPifuo\nwCgMjRyKzMuZ4gvmJFUGA7aWl2Nxmzh4WyxfzkpaKNpNvnYt8yUEBHR4qLubG5aEh+NrBVv/RIQv\nTn3RscvHTFISy0Bskx2qNLKzAW9vlpxsD8sHL0dgRCA4jnO9BH61LSHBl3tW+Tc1sbC0pUvtH6N0\nf+WGsjJMDg5GiJ3NUmNigD59gO3bRRaMD+YntJ08GhGBr0tKYFLoZsbxW8dhJCMe7PagfQO8vVnS\n16pV4grGA0eMKACY33c+8BxQVlcme96QpVdVSws67d0LXXOz3WP+8x/CggXyy56fny/Ydb1nlf+m\nTWwPsXt3+8fM6zsPewv2oqyuTDzBePBNSQmW2mn1m1m2jPlrFUleHitENG2a3UOG+PvDz80NB6qq\nRBTMeb458w2WDljqWEe15ctZzL8CH2hNTUBqqmNGVIBXAGbHzcbqc6vFE4wHG3U6TAgKgtaBjvOL\nFrGW0pWVIgomMfes8v/6a8CO0hp3EOAVgKT4JEXetLeamnC8thaJVmL7rbFgAashpsiAkq+/BhYv\n7ngXsQ0cx+HRyEhFbvwaTUasPr8aSwYucWzgyJEsOuHECXEE48GWLcDAgYCj3oblg5Zj1Rllrma+\nLSnBsnZlUToiOJhVBN64USSh+HDhglPDBFH+HMfN5Dguh+O4PI7jXrbw+USO46o4jjvV+npViPNa\no7wc2LkTePhhx8cuH7QcX51RnutnTWkpUrRa+DhYBjg0FBg3DvheaX1rzLuIjj6hASwLD8f6sjI0\nGpVVRXJ3/m50CeiCPqF9HBvIcewh+N134gjGg1WrnLpEmNJzCq5XXUeOLkd4oXhQ3NSEw9XVSHLQ\niAJYtJMic2ecFIq38uc4TgPgXwASAPQDsITjuH4WDt1HRENaX3/ie15bpKYCM2YAgYGOj50cMxk3\nqm8gV58rvGA8+Ka01GFrxYwiXT+HDgEeHqzuhoN08/bGEH9/ZOr1IgjmPN+e/RZLBzjgH2nL0qWs\nsJ2Cyj1UVABZWc4ZUe5u7lg6YCm+OausG29tWRmSQ0Ph60QvhcRE4OhRVh1YMRDJp/wBjASQR0RX\niagZwGoANvJpxcdJgxIAoHHTYEG/BVhzfo2wQvHgcn09bjQ1YVJQkFPjk5OBgwcVVu7BfJEc8Y23\nYVlEBL4psDqoAAAgAElEQVRT0B/UaGjExksbsWjAIucm6NcP0GoVVe4hNZWVWnLytsPiAYux5vwa\nRWXOf+OEy8eMry8LzkpVUhrDoUOAp6dTQ4VQ/l0BtK2KdqP1vfaM4TjuDMdxWziO6y/AeS1SWMhc\nYDNnOj/H4gGLFeX3/7akBIvCwuBupZxDR/j5Matl7VqBBXMWg4GFYi1e7PQUc0NDkVVRgRqFNC3e\nkrsFgyMHo1unbs5PsmSJolw/33zDVo3OMqLLCBhMBpwqPiWcUDzIra9HfmMjpjj7NIPiLhFbLTqy\nG98GqTZ8TwDoTkSDAHwIwKYH+vXXX7/92r17t0MnSk0F5sxx+mEIgNUoqW6qxrnSc85PIhBEhG9L\nS7HUSWvFjKJcP3v2AFFRgIWWePYS7OGB8UFBSFeI6+fbczxcPmYWLwbWr1dEM4aiIlYTKyHB+Tk4\njsOi/osUY0h9V1qKReHhThtRAAtMy80FBIy4dIrdu3fj9VdfxeuffYbXnV0B8407BTAawLY2//49\ngN93MCYfQKiVz4gPI0cSbd3KawoiInph2wv0ys5X+E/Ek6NVVdT70CHLNeEdoLmZKCyM6MoVgQTj\nwxNPEK1YwXuaVbduUaKVngZSUtVYRZ3e6kT6ej3/yUaPJtq0if88PPnHP4iWL+c/z+ni0xT9fjTv\n+5cvJpOJ4g4dokNVVbznevJJyz0NJGfPHqLBg4mIZKvnfxRALMdxMRzHeQJYDCC97QEcx0VyrYHP\nHMeNBFtxCG6y5eez4lOTJ/Ofy+z6IZn9ld+WlmJJeLhjceMW8PAA5s9XgL+ypYXFyy1cyHuq5NBQ\n7K2sRIXMlvLGixsxscdEhPiE8J9MIX6FNWsEuUQYGD4QPh4+OHzzMP/JeHCqthYGIoy0I5O8IxRy\niXhfJN7Kn4gMAH4FYBuAiwDWEtF5juOe4jjuqdbDHgZwjuO40wD+AWAxiaBV165lJWIcyN2wyrDO\nw8BxHE7cki/22kSE1LIyLHIwscsaCxYowO+/cyfQu7fjgeMW6OTujinBwfhepxNAMOf57tx3WDLA\nwdh+ayxcCGRmAjLWL7pxA7h40aHcO6twHIfF/eXfQ0stK8OCsDDeRhQAPPQQoNcD588LIJizGI3M\nRSin8gcAItpMRHFE1IuI3mx97yMi+qj1//9JRP2JaDARPUhEotROXrtWGGsFUMZNe7i6GgEaDfr7\n+Qky3/jx7Id99aog0zmHkBcJwKLwcFnLPJc3lCO7MBuJcYnCTBgRATzwAEtRl4l161j/Cz77Zm1Z\nNGARUi+kwmiSJy+DWo2oBQIZUW5uLONXVut/716ga1dmSDnJPZPhm5fHFNuECcLNuWjAIqw5vwYm\nkif22mytCIW7O1sZyeb6aW4G0tLYEkQgErVaHKyulq3JS9qlNEztORX+nrZLbDvEokWy+ueEcvmY\n6RPaB2G+Ydh/fb9wkzrA6dpaGIkwrIMy6I6weDGzY2TzCgtwke4Z5b92LfNpO1ApoEMGhA9AJ69O\nOFgofWciExHWCaz8AZldP9u3s3j2bjzCIdvhp9EgISQEG2Ry/ay7uA4P93MiC8oWKSmskIwMrp+C\nAhbNMmWKsPPKGT4tpMvHzIgRrO7ROTkCAg0G1gPCpfwZAnsTbiPXTXukuhp+Arp8zMjq+hHapGxF\nLtdPZWMl9hXsE87lYyY0lNX72bpV2HntYN06YO5cYfbN2rKw/0Ksu7gOBpO0eRm3XT4CG1EcxzKf\n160TdFr7+OEHoEcPm53v7OGeUP45OSx7ddw44ed+uN/D2HBpg+SuHzGsFUBG109jI9vIdKZWQAck\nhITgZG0tipuaBJ/bFuk56ZgUMwmdvDoJP/mCBbJoFpGez+gZ3BM9gnpgT/4e4Se3wZm6OrQQYbgA\nUT7tkU35r13LXIM8uSeU/5o17LfiRLmODukT2gdB3kE4fEO6UDUSyeVjZsECGZT/1q2spVrnzoJP\n7a3RYHZICDZK7PpJvZCKBf2E27+4gzlzWEnNRuk6y127xsKlJ00SZ/75fedj/cX14kxuhdTSUlGM\nKAAYNQqoqnK6qKZzmEOlBTCi7gnlL5bLx4zUN+2Rmhr4uLlhgMAuHzPjx7MyGJK6ftauFXSjtz3z\nwsIk9ftXNVZhT/4eJMUliXOC8HDWOmvbNnHmt0BqKlsVCrlv1pb5fedj46WNkq2ixXL5mHFzY/uM\n66V8nu3cCcTGChIqrXrlf/kyK+E8erR45zArf6kSvlJLS7FAgMQua0ju+mlqAjZvZs5kkZgZEoIj\n1dXQS5TwlXk5ExN6TECgtxOlY+1FYr9Caqqoz2fEamMR5huG7EJRIr3v4mxdHZqJMEIEl48ZyV0/\n69YJdpFUr/w3bmQ6hUe5jg4ZFDEIGk6Dk8UnxTtJK2K7fMxI6vrZuRMYMEAUl48ZX40GU4ODkS6R\n9Z96IRUP9xV+/+IO5s5l+yQS7GVcv87cPkKGSltiXt952HBxg7gnaSW1rAwPi+TyMTNmDNtvvHxZ\ntFP8iMEApKczy00AVK/8N2wQ1aAEwBK+5vWdh/UXxF/fHa2pgZebGwaK5PIxI6nrZ8MGwW5YW8yX\nyPVT01SDXdd2ITk+WdwTde7M2mhlZYl7HjAjKjlZPJePmfl952PDxQ2ir6KJCKmlpXhYZCNKo2G3\ntiSunwMHWEHEHj0EmU7Vyr+wkCV3iW2tANK5ftaJFOXTHnd3tqe4QWwjzGhk1orYT2gAs7Va7Kms\nRLXIZZ4zL2diXPdxCPYJFvU8ACTzK5hX0GIzIHwAPDWeOH7ruKjnOVdXhwaTSZBaPh0hmetHYEtX\n1cr/++9ZcwWhY5It8UDXB1DXUocLZeJu7afpdJgrsrViZu5cCXqS7t/Pkrp4xiTbQ6C7Ox4KDMRm\nkcs8r7u4Trwon/bMm8ceniJmMJeVAadOCVPLpyM4jmOGlMir6PVlZZgvgREFsFo/oq+iiQRfQata\n+UtlrQCAG+eGeX3miRr1k1NfjzqjUdA0dFtMnszC1ERtSyeRy8fMvLAwrBfR9VPXXIcdV3YgpY9E\nzeq6dQPi41lij0ikp7OOXd7eop3iDub1nSf6Kjpdr8ec0FDR5m+LuzvTQ6K6fo4dY12Z+vYVbErV\nKn+dDjh+nN20UjG/33xRN6vSdDokh4ZKYq0ArHBXQgIrtyMKIlgrHZGi1WJ7eTkaRGrunnU1Cw90\nfUCY8s32InI8ocSXCCO6jECTsUm0ZkmFjY243tiIMZ1ESL6zwvz5Irt+Nm5kF0lA3aBa5W+2Vnx8\npDvn2KixKK4txpXyK6LMn67TIVmrFWVua4jq+hHBWumIUE9PDA8IwLbyclHmT89JR3KcyBu97Zkz\nh93wIjR3r65mbYNnzRJ8aqtwHId5fcSL+knX6zFbq+XVsctRJk5kNZGKikSYnIg9/AV2c6hW+UsR\n5dMejZsGc/rMEcX1U9bcjLN1dZgULMEmYhsSEoDsbJapKDjmiyTRSsaMWFE/RpMRmbmZ4kf5tKdX\nL5b0dVj4LPPNm5nPWkIjGQBbRYvlQk1vXUFLiacne4Cmp3d8rMNcvMiK/I0YIei0qlT+NTWsnPXs\n2dKf2+yvFJpNej2mBQfDS0JrBQD8/Vm0lODl42Vw+ZiZGxqKTL0ezQJbyodvHkaEXwRigsXfvL6L\nOXNYhIPAyHSJMLrbaJTWlSJXnyvovNUGAw5WV2O6xEYUINolEsXlA6hU+W/ezIq4BYqYXGmNST0m\nIVefi5vVNwWdN12vR4rE1ooZUVw/Ilkr9tDFywt9fH3xQ2WloPOm56RLb/WbmTOHXSQBN0kbGlj1\niGQZ/iSNmwZz+8wV3PWzrbwcYwMDESB2woIFZswQaRUtkptDlcpfDpePGQ+NB2bFzkJ6jnDruwaj\nETsrKjBLYn+/maQkVmpf0BpiZpNSYpePmXmhodhQVibonLIq/6FD2QW6dEmwKbOy2LQSRRbfxZw+\nc5CWI2y0QbpeL/m+mZmAAJY8uWWLgJPm57P0axFKFqtO+Tc2sgKRKRJF2lkiJT5F0Jt2V2Ulhvj7\nQytFwoIFwsJYwU1BE0nl8ie0khIainS9HiaBLOVcfS4qGiswoov0KxkA7CEqsF9BTiMKACb2mIgL\nZRdQUlsiyHwGkwmb9XokyaT8ARFcP99/z5SdCCsZ1Sn/rCymqARqx+kUM3vPxIHCA6hqFGZ9J8cG\nVXsEdf1cu8Y6xojRYMFOYn19EezujqM1NYLMl3E5A0lxSXDjZPzJCKhZDAYgI0Ne5e/l7oUZvWcg\n43KGIPPtr6pCjLc3ukmVsGCBpCRmnApWjklEI0p1yl9mgxIAEOAVgHHdx2FrHv9OSyYiZMi4VDUz\nZw5TBoJURkhLY78CMRosOEBKaCjSBIr6kdXlY+ahh1g9k5v895v27mVJ1927CyAXD4RcRafr9bIb\nURERrIahIDl5JSXAmTPC99RsRVXK32hkCmrOHLklEe6mPV5TgyB3d8T6+goglfP06MGSSQ8cEGCy\njAx5/XKtpGi1gih/fb0eJ26dwJQYcX6EduPhwULcBMjKM3sT5GZW7Czszt+N2uZaXvMQkSx5MpYQ\nbIGWmcl2kb28BJjsblSl/A8eBLp2FaSPAW+S45OxNW8rWoz86senKeSGBQRy/VRUAEePAlOnCiIT\nH0Z26oRygwF5PBuhb8nbgskxk+HjIWFGoTUE0CxEink+I8g7CKO6jsL2K9t5zXOxvh7NRBgsUWkU\nW6SksOcz70jj9HRRQ7FUpfxF/i4coktAF8RqY7GngF9PUiUsVc2YlT+vPdKtW1nigMwrGQBw4zgk\nabVI41noTREuHzMzZgCHDgE8wljPtVZVGDBAIJl4IsQq2mz1S1UaxRaxsYBWCxw5wmOShgZg926W\nhSkSLuXPg5T4FKRdcv6mvdbQgJLmZoySOr3SCv37M8/CmTM8JsnIUNRF4uv3bzI0YfuV7ZgdK0NG\noSX8/Fgtgc2bnZ7C/DtSgJ4EwFbRmy5vgsHk/IaTkowoQIAF2s6dwLBhQIh4NaRUo/wvX2Z1SIYN\nk1uSHzFbLM5WJ8zQ65Go1UKjkF8hx7F9WqdT1FtamOWfmCioXHyYEhSE07W10DlZEnlPwR70D++P\nCP8IgSXjAU/NojQjKjooGlGBUThw3bkNp5LmZlysr8fEoCCBJXMe3spfgoukGuWfkcEUk8TVD2zS\nL6wfPDWeTrd3TFNAiGd7kpN5KP99+4DevUVt1+go3q3tHTc5Wegt7VKa9IXcOoJHVt6tW8yQGj9e\nBLl4MCfe+YSvTL0e04OD4akg5TB8OFBb62ROnskkyQpaOd9WByjNWgFYdUJnXT8VLS04WlODqTLU\nILHFuHHAlStOVidUmMvHTLKTrh8iQvplBfn7zYSFAYMGAbt2OTw0MxOYOVOaBkiOkNLH+VW0EvJk\n2sMrJ+/YMebu6dVLcLnaogrlr9cDJ0+y5iNKw9kU9a3l5ZgQFAQ/mWPh2+PhwfaYMjMdHEj0Y3y/\nwpgdEoKdFRUO1/g/VXwK3u7e6BPaRyTJeJCS4pRmMa+glcbgiMEwmow4X3beoXH1RiN+qKxEgoi+\ncWdxWvlLZOmqQvlv3swUv5S1++1lTNQY3Ky5ifzKfIfGyVmDpCOccv1cuMASMQYNEkUmPoR6emKI\nvz92VlQ4NM5cu18JESR3kZTEntAOxBPW14seQOI0HMchOT7Z4VX0zooKDA8IQIjSljJgrrWcHJar\n5RDp6ZI8oVWh/BXqTQDAqhMmxiU6VOit2WTC1vJyWWuQ2GLmTJYBWlfnwCDzRVKiokRr1I+DIZ+K\ndPmYiYtjRfhPnLB7SFYWK7KqME/jbZwJ+VSyEeXpyRpOOVQu/do11ld11CjR5DKjeOXf1MT2tuSo\n3W8vjt60eysrEe/jg0iRMvf4EhgIjBwJ7NjhwCCJrBVnSQkNRYZOZ3ehtxvVN5BfmY+x3ceKLBkP\nkpLYQ9dOlLhv1pbx0eORV56Hohr7NpxMRMhQoL+/LQ5eInZwYqIkpVEUr/z37AH69WM1M5TKtJ7T\ncPTmUVQ02OdWUFpMsiUccv2UljK3z4QJosrEh14+Pgj18MDh6mq7js/IycCs2Flwd5O+LrzdOKBZ\nzAEkCn4+w0PjgYTYBLtX0UeqqxHq4YFeSvQHt5KQwPbl7Q7MkvAJrXjlr3CDEgDg5+mHSTGTsCm3\n4/WdkmqQ2CIpiS1X7doj3bSJrW8VupIx40jCV/plGXr1OsqYMUBBAVBY2OGhR46wICGRA0h448gq\nWg1GlFYLDB5sZ2BWVRW7UNOmiS4XoHDlb65BouSlqpnkuGS7StOerauDG8ehv5+fBFI5T0wMW23Z\nlaKuhic07Pf71zTVYP/1/ZjRe4YEUvHA3Z01jrUjNEvpLh8zM3vPxP7r+1HT1HEpbjUYUQD73u1a\noG3dynaJJdINilb+Z86w+7tfP7kl6ZjEuERsy9uGJoPtQt5pOh1SQkOVGUHSDruyfRsbmVkza5Yk\nMvFhREAAqgwG5HZQ6G37le0YEzUGnbyUUXbDJna6fpTu8jHTyasTxkSNwbYr22wed6WhAbqWFoxU\nSGkUW5gvUYfbTRI/oRWt/JVWg8QWEf4R6BfWr8NCb0qOTmiPXX7/XbtYdx0V/E3mQm/pHVj/qnD5\nmJkxg2VW11oviXz1KtuWGTlSQrl4kBKf0qHfP0OnQ6JWCzcVKIf4eFbn8KStQgAtLaz/o4SlURSt\n/NVirZhJjk+2edPebGrClYYGjJOj87wTPPAAS7DLy7NxkEpcPmaSQ0ORbsPvbzAZsOnyJuWGeLYn\nMBB48EGboVkSBpAIQlJcEjbnbrZZ6C1NBf7+tnS4QNu/n23IdOkimUyKVf5FRUzpPPSQ3JLYj1n5\nW0tRz9TrkRASAg8F1SCxhZtbBzetmjZlWpkcFISTtbXQt1juw5BdmI3ugd0RFRglsWQ86ECzqMXf\nbyYqMArdA7sjuzDb4uflLS04rsDSKLbo0O8vw0VSrBZSag0SW/QN7QtPjSdOl5y2+Hl6q79fTdi8\naU+cAAICWMKRSvDRaDAlOBibrbh+FFW7315shGYpqLeOQ9haRW8pL8ekoCD4qmUpA2DsWJa/ZbED\np7k0ikv5M9RmrQC2C73VGgzYV1WFGQqsQWKLKVNYnSmLlRFU5vIxk2zF709ESMtJU5/yj4lhcZwW\nQrPMvXUUHlx2F8nxyVYLvSmxkFtHuLszY9ZiYJZMpVEUqfzr6lh5gZkz5ZbEcZLjk5F++W6LZXtF\nBR7s1AmB7gpOGrKAry/rHbJli4UP1fiEBjBbq8WO8nI0tauLk6PPQUNLA4ZGDpVJMh5YWaKpzCt3\nm6GRQ9FoaESOPueO95tNJmwrL0eiCgIM2mPVOydTZIsilX9WFttsVFBvBrsZ230s8ivzcaP6xh3v\nqyUm2RIWo34KC9lr9GhZZOJDuKcn+vv5YXe7Vohml48awnDvwoJmUWBvHbvhOA7JcXcXettTWYm+\nfn6I8PSUSTLnsVozSyYjSpHKX6UGJQDA3c0ds2JnISPnxx+ikQibysuRpLKlqpnERGDbNuCOZlgZ\nGSy2X2UrGTOWon5U6e83M3IkKx957drttxTYW8chLK2i01RsRAUFMaM2K6vNmyUlwMWLspRGUaTy\nz8xUpSv5Nslxd9602VVV6OblhWhvbxmlcp7ISLanu3dvmzfV/ITGj35/s0+5rK4MZ0vPYlKPSTJL\n5iQaDXtKt7H+VX6JMLHHRJwvPY/SulIAraVR9HrVBU205a4F2qZNLFdDhpWMIpV/WBjQs6fcUjjP\nzN4zceD6gdsp6mpK7LLGHS7lmhogO5vdtCqlj68vvN3ccKo1OWpT7iZM6zkNXu7Krk9kkzaahUj9\nyt/L3QvTek3DpsusZtbp2lp4chz6+vrKLJnz3NWGQcaLpEjlr+YbFgACvALuSFFXY3RCe8x+fyKw\nGtujR7MwT5XCcRxS2kT9qNrlY2baNODwYaCqCufPMwUzcKDcQvGj7SraXMhNlXsyrfTqxZLhjx4F\n0NDAMuRl6q4jiPLnOG4mx3E5HMflcRz3soXPOY7j/tH6+RmO44bZmk/tyh/4MU45p74etUYjhvn7\nyy0SLwYMYIr/3Dmo36Rsxdzbt9HQiJ3XdmJWrPLrE9nE358FlG/bpqrSKLaYFTsLu67tQkNLg6qD\nJtpye4G2cycwdCjr1ysDvJU/x3EaAP8CkACgH4AlHMe1L8WWACC29fULAP+xNadaapDYwpyi/n1Z\nqeqtFYApkeRkIDPNyPpqqnlTppUxnTrhemMj1uT+gCGRQxDqq+7VGYDb/jm1lUaxhtZXi6GRQ7Em\nbxeuNTZirEpKo9jitvKX2YgSwvIfCSCPiK4SUTOA1QBS2h2TAuArYhwCEMRxnNUYBJVUP7CJOUX9\n66J8pNwD1grA7tP87w4CXbsC3bvLLQ5v3N3cMEurxccF59RTyK0jEhNh2rwFuRcNSu6t4xDJ8cn4\npOC8qkqj2OLBB4HiIhMMaZmqV/5dAbTtJnGj9T1Hj7nnmBr/MC43tmCSimqQ2GL8eKD/lXTUTr5H\nFCWARG0IjjV5qt/fbyYqChV+UXh66EE5AkhEISkuCUebPFWZ2GUJjQb45agTqEEnIDZWNjkUGaT9\n+uuv3/7/iRMnYuLEibLJwge/yCnwuLQPnpzKCqtYwdMTmO+ZgZ1+q+5a2qmV8KYCtPjHIyIwRm5R\nBGOHbzIW+aQDUFFVRBtEBsbA4N8H4U35ABTcz9UBFvpmIMsnCQucHL97927s3r2blwxCKP+bANqW\nQOzW+p6jx9ymrfJXM6cNfvCsOoZLukvoG9ZXbnH4c/kygjVV+Pz0sHtG+e/MzUA0Nwjby8vxcHi4\n3OLwprER+OhGEnYaHwHwN7nFEYTtFRXoztViV95+TO4+Sm5xBCE+Jx2/Lf07ZlQDzvSjaW8Uv/HG\nGw7PIYTb5yiAWI7jYjiO8wSwGED7YgDpAJa3Rv08CKCKiG4JcG7F0mA0YmdFBeaGdra7IbXiyciA\nW0oSftjjhoYGuYURhvTL6ZgbFtFhgxe1sGsXQEOHQVNfA1y+LLc4gpCu02FeeMS98zsqLITmZiHc\nxo3B9u3yicFb+RORAcCvAGwDcBHAWiI6z3HcUxzHPdV62GYAVwHkAfgYwNM2J7VY91Rd7KqsxBB/\nfyzqk2B3Q2rFk5EB7wXJGDqURampnfzKfNyquYVne43AZr0ehnaF3tRIRgaQmOx2V7avWjGYTNik\n1+NXPYehpK4E1yqudTxI6WRmAgkJmJ3iLuslEmTrnIg2E1EcEfUiojdb3/uIiD5q/X8iomdaPx9I\nRMdsTmhHQ2qlY07smhA9ARfKLqCktkRukfih17P6/ZMn29feUQVk5GRgdtxs9PD1RXdvb2RXV8st\nEi/u6K1zj1yk7OpqRHl7I8bXD4mxifeG9d8ah5uUxKKmLbRhkARlxk2p/KY1ESGjtaSDl7sXpvea\njk25m+QWix9btgCTJwM+PnenqKuUtr16k7Vam+0d1cDJk6wEd3w82LU6dYo9tFVM28Qua+XSVUVt\nLWvZOGMGuncHunUDDh6URxRlKv99+yzUPVUPx2tqEOjujtjWGiQd9fZVBW2yhmJjWevY48dllokH\nVY1VOHzjMKb1mgYASAkNRVqbQm9q5I7ELh8f9gCw2IhBHRAR0toUcpvacyqO3jyKigZLnYVUwo4d\nwKhR7AcEdr3ksnWVqfxHjrTZkFrppOl0dyR2zYqdhR/yf0BDi0p3SZubWU3nNoXh1e5V2Jq3FQ9F\nPwR/T1Z2Y4i/PxpNJuTU18ssmfPc1VhN5Rcpp74eDUYjhraWRvHz9MOEHhOwNW+rzJLxoF3qtZyX\nSJnKX+U3rbkAlZkQnxAM6zwMWVezbIxSMHv2AH37AhE/xlh32JBa4bR1+QCtzUO0WqSp1E1y8yaQ\nn89K+9xm9mxWhK+pSS6xeGGpkFv7cumqwmhkJZzbKP9hw1iR3JwcG+NEQpnK3+xUlmsnhAfXGhpQ\n3NyMUe2Cd5PjVOz6sVCD5MEHmcIpKJBJJh60GFuwJXcLkuLvLH5jqcGLWsjMZJ2iPDzavBkeDvTr\nxx7eKsRSIbek+CRszduKZmOzlVEK5sgRdk1ifkwodHOz0d5RZJSp/GNimJVpoSG10snQ65Go1ULT\nrpBbcnwyMi5nwEQq2yW9I4TkRzQaZliq0frfd30feof0RpeALne8PzEoCOfr6lDarD7FcpfLx4xK\nV9Glzc04W1d3V2mUSP9IxGvjsbdgr5WRCsZKtT25LpEylT+g2pvWWpu5XiG9oPXV4ujNozJIxYOz\nZ5mm79e+UKt6XT/Wavd7ublhWkgINqnM9VNXx2IkZs608OEdjRjUwya9HtOCg+FloZCbagMoLBhR\nANuXP31a+sAsl/IXkIqWFhytqcE0K/W5k+OS1ZfwZTYpLZSknj6dhampKTyeiGw2bklu0+BFLWRl\nsd6wQUEWPuzblxVlOn1acrn4YKtdo1n5qyoy69o1oLTUYr16b29gyhQW8y8lylX+DzzAHoVXrsgt\nid1sLS/HhKAg+Gk0Fj9P6ZOiPovFRs3xNr1DVMP5svMwkQkDwy23uJql1WJnRQUaVLTfZLN2v7kR\ng4oMKXNplFlWqnj2D+sPN84NZ0vPSiwZDzIymJ/USklqOS6RcpW/m/pS1Dvq1Tuy60jo6nW4Uq6S\nB1pREZCXBzxkvTqk2lw/ZqvfWnMdrYcHhvr7Y1dlpcSSOYfJxDZ7bTZuUdlF2lVZiaH+/tDesXv9\nIxzHqc/100F3ndmzWXS7lIFZylX+gKoslmaTCVvLy23WHHfj3JAYl4iMyyr5IW7aZCGE5E4SE9ly\n1WCQUC4e2NOrV01RP0ePsp6wvXrZOGjsWLaCVknNLHt6XqfEq2gVXVXFeitPm2b1kLAw1iqVZ5Vm\nh1C28p86FTh2DKhQfkbf3spKxPn4oLOXl83jVGWx2NFmLiqKNfXKzpZIJh4U1xYjR5+D8dHjbR6X\nrJ+er8QAACAASURBVNUiQ6+HSQU+ZbvaNXp4sCbhKqiZZSLqcAUNAOO6j0NeeR6KaookkowH27YB\n48YxP6kNpLZ1la38fX2BiRNVkaJua4OqLVN7TsWxomMobyiXQCoe1NWx+HCLISR3ohavQkZOBmb0\nmgFPje0WV7G+vghyd8fxmhqJJHMeqyGe7VHJKvpYTQ2C2pRGsYaHxgMJsQnIyFHDjWdfQ2VzqQep\nbA5lK39AvgwIByAiqyGe7fH18MWkmEnYkqvwB5rNEJI7kbM+iSOk5aQhJd6+NjRqiPopKABu3WIJ\ndx0ycyaLB62tFV0uPrQvjWILVWT7GgzMeLVD+ffpwyJ/Tp2SQC6oQfknJgJbt7L6MgrldG0t3DkO\n/f387DpeFTetlZhkSwwbxnSKHCnq9lLbXIu9BXuREJtg1/HJoaFIU7jf3xxAYiW47E4CA1lBMYXX\nzErT6exaQQPAzN4zsa9gH2qbFfxAO3gQt8t3doDUgVnKV/6dOwNxccxqUSjmyoPWIkjakxiXiG15\n25Sbom5XCMmPmG9aJS/Qtl/ZjlHdRiHIu+OVDACM6tQJxc3NuKbglmV2u3zMKNz1k1dfD11Ly12l\nUawR6B2IUd1GYccVBT/QHLxILuXfHoX7Fb53wFoBgAj/CPQN64s9+QqtuXLkCAs/6NnT7iEKv0QO\nuXwAQMNxSGzd+FUi1dXMqJw+3YFBSUksgkuhOQxpej2SQkPhZqcRBbRG/Sh5Fe3AChpggVn5+cCN\nG+KJZEYdyt9sViow+qKgsRGFjY0Y62AX5pT4FOVm+9oR5dMeuVLU7cFgMmDT5U0dhni2R8kNXrZv\nZ4oiIMCBQT16AJGRLOxQgTji7zeTFJeEzMuZMJoU+EDLzWUlO4cNs3uIuzswa5Y0gVnqUP4DBzJX\nxPnzcktyF+k6HRK1WrhbydyzhqJT1J1Q/nKlqNvDgesH0D2wO7oHdndo3LSQEBypqUFlS4tIkjlP\nWpqDLh8zCnX96Jqbcbq2FlPaFXLriOigaHQN6IqDN2Rqh2UL80VyYCUDSHeJ1KH8FZyi7sgGVVv6\nhvaFp8YTp0sUVnPl6lVAp2ORPg6iVNePoy4fM34aDcYHBmJrubLCcltamPcmxfE/SbG/o0y9HlOC\ng+Fj1+71nSg2d+b774E5cxweNmMG6/QodmCWOpQ/oEjNUtHSgiM1NZhupZCbLRSbop6RwSKsHFzJ\nAPKkqHcEETHl38cZTdma7aswX9bevUDv3nYFkNzNiBEsaTI3V3C5+JBmZ56MJRT5OyopAc6dAyZN\ncnhop07A6NHMtScm6lH+EyYAly4BxcVyS3KbzR0UcuuI5HgFVvl0wuVjJjwc6N9fWb1Dzpedh9Fk\nxOCIwU6NT9RqsbW8HC0K6lbvpEHJkLN7iBUajEbsqqiwWRrFFsM6D0NNcw1ydAqKNc7IYLkVHWT8\nW0OKBZp6lL+nJ1sPbdoktyS3cWaDqi3juo9DfmU+blRLsLVvD5WVrFjM1KlOT6G0BVrapTSbhdw6\noouXF2J9fLCvqkpgyZyDiKfyBxTn+smqqLBZyK0j3Dg3JMclK6tmFs+LlJgofmCWepQ/oKibtslk\nwvbyciQ5uVQFAHc3d8yKnaWcFPXMTLZM7SC13hZK6x3irL+/LUoq9HbiBODjw8r0O82UKWwihbiz\nnN03a4uiVtE1Ncw3l2BfQqEloqOBrl1ZOK9YqEv5JyQAP/wA1NfLLQl+qKhAPz8/RHjarhPTEYrK\n9t24EZg7l9cU5t4hZ84IJBMPimqKkFee12Eht44wl3pQQmSW2aB0ciHD8PFhD3kF1MwyEiGDh7/f\nzKSYSThTcgZldWUCScaDbduAMWNYVjUPxLZ11aX8Q0KA4cMVkaKeptdjDs8bFgBm9J6BA9cPoKZJ\n5iJiDQ2sno9T8YM/wnHKcf1k5GQgITYBHhrn3AlmBvj5gQCcr6sTRjAe8Hb5mElOZqGIMnOouhrh\nnp7o6ePDax5vd29M7TkVm3MVEGss0EUyXyKxbA51KX8AmDcP2LBBVhFMREgXYKkKAJ28OmFM1Bhs\nuyJzO6zt29mDlccehpmUFHb/y40QLh+gNTJLq0WazG6SvDygrMzOQm4dkZzMrrnM5Sv47pu1RRGr\n6JYWluziZNBEW4YPZ5fn4kUB5LKA+pT/nDnMNy1j4s2xmhoEaDSI5+Ebb4siQtUEcPmYGTcOKCxk\nVSfloqapBvuv78fM3h2XpLYHJfj909LYg9WJKNy7CQtjmacyr6KF8PebmR03G1lXs9BoaBRkPqfY\ns4fVIuvShfdUHMd+kmLZuupT/lFRLMhZxnhCIW9YgKWob87dDINJpnZYBgN7oAriT2Ap6klJ7Hki\nF9uubMPoqNHo5OVY2Q1rjA8MxOWGBtySMYlBMJePGTE1ix1cqqtDrdGI4Q7VqLBOqG8oBkcMxg/X\nfhBkPqcQ+CLNmyfe70h9yh+Q/aYVWvlHBUahe2B3ZBfK1A5r714gJoY9WAVCbu/cxksbMSdeuB+h\nh5sbZoaEIFMm109pKXD2LKuhJBhz58q6ik7T65HsYCG3jpB1FS1IHO6dmFfR+fmCTXkbdSr/efPY\nlyxD4k2ug2Vn7UXWm1ZAl4+ZKVNYxE9JiaDT2kWToQmbczdjbl9h/yY5G7xkZLA0FydzhiwTFcUe\n+nv3Cjip/WwoK8NcAY0ooPV3dDkdJpIhKe/4cdaqsU8fwabUaNj2gRjWvzqVf1wci/yRoTrhBp0O\nc0JDoRHQWgF+rPIpeTih2VoRWPl7e7PIXDkCSnZe24kB4QMQ6R8p6LwzQ0Kwp7ISdTKURBbc5WNG\nTL+CDQobG5HX0IBJdnSKc4Q4bRwCPANw4tYJQee1C5EukliXSJ3KH5DN9bO+rAzzw8IEn3dI5BA0\nGhpxSXdJ8LltcuwY4OfHM2vIMnK5ftZfWI95feYJPm+whwceCAhAVkWF4HPboraWbXHNmiXC5GbN\nIvEqeqNOhyStFh6C7F7fiWyraJGUv1iraPUqf/NNK6GlfL2xEVcaGjBRYGsFaA0njJPhphXB5WMm\nIQHIzmZVI6TCYDIg/XI65vUVXvkDQIoMUT/btrFCXzxzhiwTH88mPnJEhMmts76sDPNEMKIAmZR/\nbi7LmB45UvCpvbzEWUWrV/kPGcIKX5w9K9kpN5SVITk0VBRrBfjRXykpIip/f39g4kRpGlOY2Vuw\nF9GB0YgOihZl/iStFpl6PYwSGh0bN4rk8jEjseunpLV2/3QHa/fby+huo1FUU4SCSgljjTdsEDAO\n927EuETqVf4cJ7lfYb1Oh/kCb1C1ZWKPiThfeh4ltRLtkl66xOqQjBgh2imkdilvuLgB8/vOF23+\nGB8fdPHywn6JCr01NbECX/PEWcgwzL8jiR5oaTodZoaEwNvJargdoXHTYHbcbGmt/3XrgAULRJs+\nIQE4cEDYVbR6lT/ALFaJNEtxUxPO1tZimhO1++3Fy90L03tNR+ZliUxls0kpkrUCsHj/rCxpyjGZ\nyISNlzaK5vIxsyAsDKmlpaKew8yOHcDgwUBEhIgnGTqUhXtK1ClPrH2ztsyJn4MNlyQyDPPz2WvC\nBNFOYV5FC1nUWN3Kf/RotguSlyf6qTbqdJil1cJLREUJAPP6zsO6i+tEPcdtRHT5mNFqWVOwbRJU\nrzh84zCCvYMRHxov6nkeDgvDBp0OJgks5XXrgIcfFvkkYqeStqGipQWHqquRIKIRBbCaWaeKT0mz\nil6/nhlR7u6inkZoR4e6lb9Gw750Caz/DTqd6NYKACTGJSK7MBvlDSK3DpTAWjEjlXdu/cX1olv9\nABDn64tQDw8cENn109zM4vtFdfmYkegipev1mBwcDH+RFaW3uzdmxc7ChosS3HiSPKGFX0WrW/kD\nkrh+9C0tOFJdjZkiWysA4O/pjykxU5B2SeQA+dRU9t2J/CME2PN50yamzMSCiET397dlQVgY1pWJ\nWz54504WgStAmZiOGTMGuHWL9XAWkfVlZZgn4r5ZWxb0W4DUC6ninqSwELh8WeDUa8totWx7Tqj2\njupX/pMmsY3LmzdFO0W6ToepwcFOt2t0FElu2tRUUTeo2tKlC4so3LVLvHOcKj4FjuMwKGKQeCdp\nw8Otyl9M149EBiXDvIpeJ57LscZgwO7KSiQJVMWzI2b0moETt06gtE7E/ZkNG1gKrpNdyBxl/nzh\nLpH6lb+nJ/vy168X7RRSbFC1JTEuEfuv70dFg0jJRGaXz8SJ4sxvgYcfZs8bsVh/cT3m953vdLtG\nR+nr54dgd3ccqq4WZf6WFhbXLYnLx8yiRcDataJNv7m8HGMDAxEkkaL08fAR3/Uj6ROa3Q+ZmcJU\n4la/8geAhQuBNWtEmbraYMDeqiqnm0s7Q4BXAKb0nCJeWzoJXT5mFi5kCZBiuH6ISDJ/f1seDgtD\nqkiun927WfHa7t1Fmd4y48czN8aVK6JMv76sTNRQaUuIuoouKgLOnePV89pRIiNZJe6tW/nPdW8o\n/6lTgZwcduMKTKZej/GBgegkoaIERL5pJXT5mImKYvWusrKEn/ts6Vk0tDRgVNdRwk9ugwXh4aK5\nfiQ2KBnu7syvIIL1X280Ynt5uaDVcO1hZu+ZOF50XBzXz8aNrNO6oNX2OkaoBdq9ofw9PZm/UgS/\nwprSUiwMDxd83o5IikvCvoJ9qGwUuDaCDC4fM2It0NacW4OF/RdK5vIx08/XF/4aDY4I7PoxGJhe\nmS/N3vWdiOT62aTXY2SnTgjj2fPaUXw8fJAQm4CNF0UICpHlCc1cP1u28I/6uTeUPyCKZqloacHu\nykrJrRWAuX4mx0wWPuonNVWSmGRLLFjAevsK2Q+FiLDm/Bos6r9IuEnthOM4UaJ+9u1j7p6YGEGn\ntY9x44DiYhbBIiCrS0uxWAYjChBpFV1SApw8CUyfLuy8dhAWxkoIbebZrvjeUf6TJwPXrrGXQHyv\n02FKcDACZVCUgEg3bWoqe1DKQJcuwKBBwiZ8Hb91HBzHYVjnYcJN6gDmqB8hS3GnpspiUDI0GnZy\nAa3/aoMBWRUVgtfut5eE3gk4VnQMZXUCPqQ3bGA1F3g2nneWhQv5XyJeyp/juBCO43ZwHJfb+l+L\nlZo4jsvnOO4sx3GnOI47xuecVnF3Z+shAV0/a0pLsUgmawUAkuKTsLdgr3CuHxldPmYWLRJ2gbbm\nHLP6pXb5mBno5wcvNzccrakRZL6WFuZNWCT9QuZHhNAsbUjX6TA+KAjBEkX5tMfHwwcze8/ExksC\nun6++w5YskS4+Rxk7lxmRNXWOj8HX8v/ZQA7iSgWwM7Wf1tjEhENISLxqogJ6Popa27GwepqSaN8\n2tPJqxMmxUwSrkCVjC4fM/Pns4QvIULViAhrL6yVxeVjhuM4LAwPxxqBav1kZQG9esnk8jEzdiwr\nT3zxoiDTyenyMSPoKrqwkNVBmjFDmPmcQKtleXl8av3wVf4pAL5s/f8vAYhZeLZjJkxgyV4C1PpZ\nX1aGWVqtZIld1ljcfzG+O/edMJOtXSt5lE97IiKA4cPZhhVfDt04BH9PfwwIH8B/Mh4sDQ/H6tJS\nQco8y2xQMtzc2H0igPVf3tKCfVVVSJbRiAKAhFjm+hGk1s/q1cz0ljjKpz18bV2+yj+CiG61/n8x\nAGu1BwlAFsdxxzmO+wXPc1pHo2GmpQDWvxKsFYDV+D9YeJB/qFpuLnDjBsuIlhmhXD+rz/3/9s48\nOooq++PfRwARUJYkJBDAiIDKEhbhJ4oDjAIioAgoYtgNKu4yOhzU48A444yKgmiQIPsaZMcBBAYF\nSYCEsAVISCAEJISErGQjSXe6vr8/XsIEyNJdVd3VTfpzTp90V9V7fdNdfd99991731pDXT5lPNyg\nAZrVrYv9GuvtFhbKWj4GLcncjE6un80ZGRjQpAnuMXC2CQD169THs+2fxU8xOtx4TjFCy0n8r7/K\nquxqqFb5CyH2CCFOV/AYVv46yhWvykyfJ0h2BfAMgLeEEH2qes+ZM2feeOzbt8/Kf6UUHULVrhQX\n42RBgUNq+VRHg7oNMLT9UKyL0fhDXLNGfjYG/wgBuTSzaxdQUKC+D4tiwfrY9Ya6fMoT2KwZ1mh0\n/WzfLmu3+Oq79bA6evWSWuX0aU3dOIsRBQCBnQOx5tQabZ3Ex8saSAaumwHAvn37MHfuTPj4zMSE\nCTPVdUJS9QNAPIDmpc+bA4i3os1MAB9WcZ6asFjIFi3I2FjVXXyblMQJGtrrzY6zO9hrUS/1HSgK\n2a4dGRmpn1Aaefppcu1a9e33XdjHLvO76CeQRi4VFrJpWBiLLBbVfQwfTi5erKNQWvnwQ/Kjj1Q3\nv1pczEb797OgpERHodRjtpjZbFYznss8p76TGTPI997TTSatrFxJDh0qDW/aqL+1un1+BjCh9PkE\nALcFpQshGggh7il7DmAgAG3mRFXUqiWnZKtXq+7CmawVAOjfpj/OZ51HYrbKiotHjshdmnr21Fcw\nDQQGavqKEHo6FKM7jdZPII20qlcPnRo0wM4sdaW4c3LkFN6htXyqY9w4+SWp3Nx9Q3o6hnh6or7B\n62Zl1K5VG6M6jFJv/ZNO4/IpY9gwYP9+dW21Kv8vAAwQQpwD0L/0NYQQLYQQZSkIPgDChRDRAA4D\n2E5Sh8oUVTB2LLBqlaqbNrGwEAmFhXjKTvuLqqGORx282OFF9TftmjVS2xrsGy/PiBHyplWTH1Vc\nUoz1sesR2DlQf8E0EOjjgzVX1S0obt4sl2MaN9ZZKC0EBMjN3cPCVDVfc/UqXnYiIwoAxgSMwZpT\na9TlZRw/LtOv7bBJu1ruuUemG6hBk/InmUnyKZLtSPYnmVV6/ArJwaXPE0l2KX10JPm5lve0ii5d\n5Kdy4IDNTVddvYrRzZrZbZN2tYwJGIPVp1bbftNaLDI6YcwY+wimkoYNgSFD1C3PbD+3HQE+AWjd\nyJFVz6rnBW9v7MzKQl5Jic1tncyg/B/jxklDykbOFxbiXGEhnnaCdbPyPOr3KMyKGcdSjtneODQU\nGD3aqYwoQNp1anAuDacXQkjrf+VKm5qRxMqrVzHerhumquOxlo+hqKQIJ1JP2Nbwt9+Ali2B9u3t\nI5gGxo2z+SsCAKw8uRLjAsbpL5BGPOvUQZ/GjbElI8OmdmlpQGSk3KnJ6Xj5ZVkuvajIpmYrU1Od\n0ogSQiCwUyBWn7LR56go0ohywhF60CB17Zzrm9GTwECbb9qI3Fx4AOhxzz32k0slZTetza6fMpeP\nE9K/v0w4PnfO+jaZ1zOx98JevNDBqPoHVRPYrBlCbYz6CQ2Vir9+fTsJpYWWLeUG79u2Wd3khhHl\nFGFLtxPYORBrT6+FRbFY32jfPplZ1cnYnJKKUFsr785V/q1aSfePDSlwK0pvWKPjxisjsHMgQk+H\nWn/TFhbKHUFGO8/CaHlq15aGlC1ehXUx6zCo7SDce9e99hNMA895eeFQbi6u2rBxwbJlwMSJdhNJ\nOzZO0Q7m5uKuWrXQvWFDOwqlnoe9H4ZvQ1/su7jP+kbLlzv5l2Q7d67yB2zyVxYrCtalpWGsE7p8\nyujYrCO8G3hbf9Nu3SoDx5s3t6tcWihbm7d2KcNZXT5lNPDwwPNeXlhl5cJvdLSspOAEuXeVM2KE\ntHytdGetTE3FeB8fpzWiAGBM5zFYdcpKqyMvT/6WnHQGrZY7W/mPHAns3St/XdWwPTMTXRo2ROt6\n9RwgmHomdpmIpSeWWnfx0qXApEn2FUgj3bvLLPlDh6q/NiErAeezz2PgA44vo2sLk3x9sTQlxarF\n+eXLgfHjZYSy03LvvcDgwVYVTSyyWLA+PR1jnNiIAmQAxeYzm5FvsqIy2saNsnSMk0UuacWZbznt\n3HuvXA2xIqRkRWoqxjn5DQvIm3bb2W3VV/pMSpLx/c8bW26pOoSw3quw6uQqjO44GnU8jKkOaS1/\natQIhYqCo9Xk3ZvNMox+woQqL3MOrAyg2J6Vha4NG6KVkxtRvg190de/L9bHWFHsbdkyF/mSbOPO\nVv6A/NKWVm0pZ5hM2HftmkM3aVeLV30v9G/THz+drqZGyYoVsj6LQfXGbWHsWDk+V1XpU6GClSdX\nYmzAWMcJphIhBCb6+mJpamqV1/3yC9CunXw4PQMHAomJsrxBFbiKEQUAk7pOwpITS6q+6MIFWcFz\n6FDHCOVA7nzlP3CgrMURHV3pJaFpaRjs6enwfXrVMqnrpKpdP6S0Vpzc5VNGq1Yy+XjTpsqv+f3i\n72hQpwF6tLBfRXA9meDri7VpaSiyVL44v3y5CxmUdepI/9SSypXlVZMJ+3NyXMKIAoAh7YbgbOZZ\nnM2sYteyFStkwISDt590BHe+8vfwAF55BVi8uMLTJLEoJQVBTrwoeitPt30al3IuITY9tuILDhyQ\nP1YnKudQHZMnV/oVAQAWHV+Eyd0nO/UiYnla16uHbg0bYmsl602ZmbKcg1NU8LSWoCA5YpnNFZ5e\nkZqK4V5ehlfwtJY6HnUwtvNYLDuxrOILSKn877AonzLufOUPSAt4zZoKY/6j8vKQb7Hgz06VV181\ntWvVxvgu47H0eCXWf9lCr4soSgB47jlZQPL8+dvPZRVmYfvZ7S7h8inPpObNsawS109oqFxDbdTI\nwUJp4cEHpY+qgvDpMiPqVRcyogBgUrdJWBG9ouLw6fBw6TbtbswWofamZih/f3/5BW6+fRu3hSkp\nmNy8OWq5kKIEpOtn1alVMFtuscIKCqT/ZJzzhkNWRN260vdfkVdh9cnVGNxuMJre7VylAqpjuJcX\nInNzkXzLjvUk8OOPckLqckyeDCxadNvh/Tk5qC0Eet3rnPkXldGpWSf43euH3ed3336y7EtyMd1g\nLTVD+QMV3rR5JSXYkJ6OiU6aiVgVD3o9iDZN2uCXhFu2xAoNBfr0cZKi8LYRFCSXKsqXxiF5w+Xj\natT38MAob28sTUm56XhEhFzcfvJJgwTTwgsvAAcPyh3zylFm9buKW648FS78ZmbKrOY71OUD1CTl\nP2wYcPLkTX6FtWlp6Ne4MZobvB2bWoK6BWHhsYU3HwwJAd54wxiBNNKxI9C6NbCzXM3XoylHkW/K\nRz//fobJpYXXW7TAjykpN23xuGAB8NprTh7bXxkNGsiFimXLbhzKNpvxn4wMp06QrIrRnUZjT+Ke\nm3fLW7FCRvg4WWE6PXHF208dd90l/Qrlwj4XuqCPsjyjO43GoaRDuHjtojwQFSUtloHOnQRVFbdO\n0BYdW4SgbkGoJVzzVu12zz1oUbcudpQu/GZnA1u2uLhBWbY6X1oyfdXVq3jG0xNeLhoR07heY4x8\neCQWHyuNOCDlCP3668YKZmdc8xellqAg6VQ2mxGdn48Uk8npSs7aQv069TG+y3gsOLJAHggJkTes\nS5qUkpdekuXjL10CcotzsS5mHSZ0cZV4yIp5w88P869cASANysGDAReJhqyYRx6RGw/s3u2yC723\n8mbPNxFyNEQu/P7+u4wS7N3baLHsiutqCTV06iSjFTZtQsiVKwjy9YWHC/ooyzOlxxQsObEExemp\ncqHXJVcR/0fDhnKtOiQEWBm9Ev3b9IffvX5Gi6WJUd7eiMrLQ+L1wjvDoBQCePttIDgYB3NzcV1R\n0M+FouUqonvz7mjesDl2nNvxP6vfxXVDdQhVO9rYESEE7SrThg24tmAB7p8xA7E9e7qsv788A1YO\nwL9jmqPHH2a54OvinD0L9H6C8Py0AxY8G4K+/n2NFkkzHyQkIDVZ4NiUBxAbewfolcJCoHVrjN62\nDY/5+eG9li2NlkgzK6JXYMeBZVj7yXGZzexEu/lVhxACJG26q2qW5Q8Azz+Ppf7+eEaIO0LxA8Cb\nj7wBzxUbgClTjBZFF9q3B/z7/YaCvNroc18fo8XRhddbtMDGglQETVFcX/EDwN1348qUKdidk+OS\n0XIVMarjKHTcFoncZ55yKcWvlhqn/BUPD8wbMQLv/Oc/RouiG89duQdmxYzo9q6UMVQ1tXsHo/ax\nt10ydLAi6mfVh+VcAzR6TsWmxU5KyIgRePm339DIhr0LnJl69MA7UbWw+E8NjBbFIdQ45f9LVhYa\nN2mCXgsWWFXq2RXwmPsdzo4djOCoeUaLogt/XPsDZ4v3Q4keg8OHjZZGH+bNAwZdb4kfsy6r2zzc\nyShWFPx4/TreTk2V2fN3Ahs34q6HOuJf+Ttw3XzdaGnsTo1T/t9dvox3/P0hnnuu6mIyrkJ8PBAZ\niUenf48NZzbgar51m4g4M/OPzMe4gHF4+7WGCA42WhrtXL8uw1dnv+iJnJIShOXkGC2SZjakp6NT\ngwZ4+OWXgeBg63fjcVZIYM4c3P3hR+jdqjeWn1hutER2p0Yp/zMFBTiRn4+XvL1ltMK8eTenk7oi\n334LTJkCb6/WeKnjS5jn4tZ/XnEeFh1bhPcefQ9BQTLJ8pZkUpdj5UoZNdiurcDUli0xOynJaJE0\nQRJzkpLwbsuWwIABQHGx3DTJlYmIkDuVDR2KDx//ELMjZtu2x68LUqOU/6ykJLzt54d6Hh6y4qW/\nv1UbvTgtGRnA2rXAm28CAKb2moqQIyEuPWVdeGwh+rfpj/ub3I+mTWUV4blzjZZKPYoix+f335ev\nJ/j64mBuLs5dd93vaO+1ayhQFAz19JQ5JX/9K/DVV0aLpY1vvwXefRfw8EDvVr3hebcnfo7/2Wip\n7AtJp3pIkfQnqbCQTcLCmGky/e/gjh1kQACpKHZ5T7vzz3+SEyfedOjZNc9yftR8gwTShqnExFaz\nW/FI8pEbxy5eJJs2Ja9dM1AwDWzfTnbpcvMt9sn583wzPt44oTQy8MQJLr5y5X8HiorIFi3I48eN\nE0oLFy7Imywn58ahdafX8fHFjxsnk42U6k2bdG2NsfznXL6MCb6+aFqn3BaAgwbJv+WLybgKhYXS\nbTV16k2HP3jsA8w+NBsKFYMEU8/a02vRzrMdHmnxyI1j990HPPOMzLtxNUjg88+B6dNvjut/WNja\npAAAE9JJREFUy88Pa9LSkOGCUTLH8/IQU1Bw8x69d90lpzazZhknmBa+/FImdZWrSDr84eFIyUvB\nwaSDBgpmZ2wdLez9gB0s/yyTiU3CwnipsPD2k6tXk3366P6edic4mHz22dsOK4rCnj/25PqY9QYI\npR5FUdj5h87ceW7nbedOnJCGZVGRAYJpYO9esl07sqTk9nOvxsXxk/PnHS6TVl6OieGsP/64/cS1\na6Snp7SiXYnkZLJJEzIt7bZT30V8x2GhwwwQynbgtvwr5ocrV/Csp2fFm0qPGiULyUREOF4wtZhM\n0lr55JPbTgkh8GmfT/HZ75+5lPX/S8IvEEJg4AO3F6Xr0gUICLBuk3dnoszq9/C4/dxHrVtj/pUr\nyK5kVyxn5EJhIXZnZeG1Fi1uP9mokSz49s03jhdMC998IxeWKii2FNQ9CJHJkTiResIAwRyAraOF\nvR/Q2fLPM5vZLDycp/PzK78oOJgcMkTX97UrCxeSAwZUelpRFHZf0J0bYzc6UCj1KIrCHj/2qHK2\nEhZG+vuTxcUOFEwDERFk69ZVyzvpzBnOSEx0nFAamRwXx4+rmq2kpEgrOinJcUJpIT29WnlnH5zN\n4WuHO1AodcBt+d/Od8nJeLJJE3RsUEXWXlCQrPXvCtZ/SQnw738Dn35a6SVCCMzoO8NlrP9tZ7fB\nZDFhxMMjKr3miSdk2YelVexb70x8/rkMgqmqyvHHrVsjODkZOS4Qbny+sBCb09PxQatWlV/k6yt/\nS//6l+ME08LcuXJzmirqEr3e43UcunwI0anRDhTMQdg6Wtj7AR0t/2yTiV7h4YwrKKj+4pCQKq1p\np2HxYrJv32ovUxSF3UK6cfOZzfaXSQNls5RNsZuqvTYykmzZkqxo6caZOHxYrlFcv179tWNjY/nP\nixftL5RGxsfGWjdLSUuTkTPO/j+VyWnFuss3B7/hiJ9GOEAo9UCF5W+4sr9NIB2V/98SEzkhNta6\ni4uLpV/h9991e3/dKSwkW7UiDx606vLNZzaza0hXWhSLnQVTz5YzW9g1pCsVK8Nthw4lv//ezkJp\nQFHIJ58kFyyw7vq4ggJ6hYffHILsZJzJz6dXeDivmc3WNfj4YzIoyL5CaWXqVPKtt6y6tMBUQN+v\nfXki5YSdhVKPW/mXI8NkYtOwMJ63xvwqY8kS8k9/ct64/6+/JodZH32gKAofXfgoV0avtKNQ6jFb\nzOwwrwN/jvvZ6jZHj0qr2prJnBHs3i0jfGzR5a/FxfGDc+fsJ5RGXjp92rbZSWamjPxx1v/pjz+k\n1Z+SYnWTuRFzOWjVIDsKpQ238i/H22fP2p5IYzaTHTqQW7boIoOuXLtGenuTp0/b1CzsjzC2ntOa\n1002DIIOIiQqhP2W9bPa6i/jxRfJf/zDTkJpwGIhu3cn162zrd2VoiI2DQvjBVsMFQdx8No1+h04\nwPyK4lWr4h//IF94wT5CaWXSJPKTT2xqUlxSzLbfteWuhF12Eko96QXpbuVfRkzpNDVdTWjIrl1k\n27bOF1Q+ffpt2bzWMnztcH4R9oXOAmkjpyiHvl/78uiVoza3TUyUhtvly3YQTAOrV5OPPCIHAVv5\nW2Iix1rronQQiqLw0SNHuMwGC/kGBQUy3MnZ3KjR0WSzZqpSxjfGbmTA/ACWWGwcCO3MG9vecCv/\nMp6JjubsS5fUdzBkiHSxOAvx8XIanZysrnlGPD2/9GRa/u2JLEbx8Z6POX7zeNXtP/qIHK++ue7k\n5pJ+fmR4uMr2ZjObHzjAyHIlBoxmTWoqH4mKokWtGzQ0lOzWreIsNyNQFOnWDQlR2Vxh78W9ufjY\nYp0FU8+pq6fo/ZW3W/mT5C8ZGWwXEcFiNeZXGWfOkF5eFWb9ORxFIZ9+mpw1S1M3U3dO5cQt6mYO\nepOQmUDPLz2ZlKM+Hjw3l2zeXEYAOQPTpmkfjFakpLB7VBRLnGDNKb+khK0PHuT+7Gz1nSgK+fjj\n5KJF+gmmhdWrpV9Ow2B0+PJh+n7ty8zrmToKpg5FUfjk8if5XcR3buVfUFLCNocOcXtGhuo+bvDB\nB+SYMdr70cqmTeTDD9u2glgBuUW5bDW7Ffdd2KeTYOpQFIUDVgzgrAPaBjOSXL5cGpbWBqHYizNn\n5MRMjXekPIqisO+xY/zeCZKk/pqQwMCYGO0dRUWRPj7GG1JlUzMrI+Wq4q3tb/HVn1/VQShtrDix\ngl3md6GpxORW/tMSEjhajxuWJPPzyTZtZOVPo8jNJe+7j/z1V1262xS7iQ8FP8Qis3HrGWtOrmHA\n/ACaSrSHNiqKTM34wsDlDIuF7NePnD1bn/7K1quuGLjmdCw3l83Cw3lVr3Tqv/yFDAzUpy+1vPuu\n6jWzW7lWeI1+3/gx7I8wXfpTQ3pBOn1m+fDw5cMkWbOV/7HcXHqHhzNVz/z///5XLlrl5urXpy28\n9pqu8dKKovC50Oc4Y+8M3fq0hbT8NPp+7ctDSYd06zMxUVrdZ8/q1qVNBAeTvXrp69b++Px5Pn/q\nlM1RUHpgtljY48gRLi1fslkrZYbUtm369WkL+/bJ+OBM/Vw162PWs8O8Diw0G5NxOH7zeL73y3s3\nXtdY5X+9pIQdIyO5XOu8uyImTiSnTNG/3+rYuVMOPDovAF7Oucxms5oxIilC136rQ1EUDgsdxmm7\np+ne95w5ch3P0euKCQly4ImL07ffIouFAYcP66uAreRviYkceOKE/gPPr7/KBEUtawhqyMuTA8/P\n1ueSWIOiKBzx0wj+ZedfdO3XGjbEbOADcx9gXnHejWM1Vvm/FR/P0TEx9rGUrl0j77+f3OjAImlp\nabKOwX//a5fu18esZ9vv2t5089ibhUcXssv8LnZxOZWUkH/+Mzlzpu5dV4rJRD72GPnNN/bp/2Re\nHr3Cw5nowNj/sOxs+tjT5fTOO+TIkY5NonzlFXLCBLt0nVGQwZazWzo09j8pJ6lC461GKv+t6en0\nP3SI2fZMj4+MlLHBjqhXUlIiHdnTp9v1bSZumchxm8Y5xLVwMvUkvb7y4umrtiWo2UJyMunrK2f4\njmDqVBkRrCWorDpmX7rEHkeOsNABU5oMk4n+hw5xa3q6/d6kqEhG28ybZ7/3KM/SpeRDD0nr307s\nOb+HLb5pwSu59p+lmUpM7LesH//5+z9vO1fjlH9Mfj69w8N5yBF7/H39Ndmjh/3rCvztb3IF0c4h\nLPnF+ewa0pVzDs2x6/tkXs9km7ltHFJiYudO6drVkuJhDRs2yDJQOrqQK0RRFI46fZoTz5yx6yBt\nslj45PHjjikxce6czFQ/cMC+7xMdLcO1bcyIV8PMvTPZa1EvuwdSvLPjHQ5aNajCJLMapfwzTCa2\nOXTIPn7+ilAUGcg9YoT9zL1Vq6Sf30H/08Xsi/T92tdu01ZTiYkDVgxwqF901iy5LbO91ugjIqRO\niYqyT/+3kl9Sws6HD3OOHUe0t8+e5aDoaMflF2zfLqdp9trJLClJri+Ehtqn/1uwKBaO/GkkJ26Z\naLdBetHRRXzw+weZXVjxmkmNUf65ZjN7HT3KaQkJ1V6rK0VFcsvH997T32+5a5d0LTnAUinP/ov7\n6f2VNw9e0h7/XB6LYmHgxkAOXTOUZovjAvEVhXz9dZkXp7frOj5e6ixHB61cLCxkq4MHucIORsG/\nLl5kh8hI+7pNK2LePPLBB/WP/8/OJjt3Jr/6St9+qyGvOI/dF3TntN3TdB8AtpzZQp9ZPoxLrzyy\noEYo/zyzmX2OHeNrcXGGhMIxM1P6LfUcAPbskVPhMGPihnec3UHvr7x5JPmILv2VWEo4eetk9l3a\n15CCciaTrCk2aJB1NfWtIS5OTsqMSlaNzc+n74ED3KCjspxz6RLbRkQw2aicgk8/lYUU9RrUMjPJ\nnj3tY5xZQUZBBjv/0FnXUGprf5sOV/4AXgQQA0AB0KOK6wYBiAeQAGB6NX1W+g+mFBWxe1QUJ8fF\nqa83ogdZWfIme/VV7fsKbtwoFb/BBbA2n9lM76+8K9xA3RYKzYUc8dMIPrX8KeYWGZQfQblkMnq0\nrK2flaWtr6goWUpiyRJ9ZFPLsdxcNj9wgD9orGhnURROP3+e7SMieNHonXE++4xs317GzWrh0iVp\n8X/4oaEl2VPzUtlxXke+s+MdzQXglh1fRp9ZPlbNyo1Q/g8DeBDAvsqUPwAPAOcBtAFQF0A0gA5V\n9FnhP7c/O5utDx7kZxcuGGPx30pODvnsszLAXE3BNbNZbnrRqhV5pOJRfe/evdpktJGwP8LoM8uH\nX4Z/qWoDmHOZ59gtpBtf3vCyrotfaj8Hs5l8/33ygQfk+p+tKIrcOM3LS1bZcAZW7dzJ9hERfC0u\njgUqooAyTCYOO3mSjx09qq7qrT344Qfp8ty+3aZmN+6L336T/rhZs5xiL47swmw+tfwpPr3yaabk\n2T6rKTIX8f1f3qf/t/6MTbOu0qthbp9qlP9jAHaVe/0RgI+q6OumfyrLZOJfzp2j74ED/I89w9DU\nYLGQf/+7tNx//NH6CJ3ISFmUZuDAKn2eM2bM0EdOG7iYfZFPLHmCfZf2tXrnouKSYs46MIteX3kx\nODJY98FZ6+ewcqVU4J9+KpNNrSExkRw8mOzYkXSmSsszZsxgjtnMwJgYPhQZyV1WhhxZFIVrUlPZ\n8uBBfnDuHIvsGaOqhv37pV9twgTy6lWrmsyYNk0mYDZvbrecGLWYSkz85NdP6DPLh8uOL7N6FrD3\nwl4GzA/g8LXDbSoe56zK/wUAi8q9HgcguIq+qCgKT+Xl8a8JCfQOD+drcXH6lm3Qm+hoOQN44AHy\nu+8qLjSfn09u3SpXIps3lxqpGiVphPInpc8+ODKYPrN8OPKnkdxxdkeFlvyF7Av8IuwLtp7TmoNX\nD2Z8ho2b51iJHp/D5cvkqFHSwJw5Uyr0Wz9+k0kakWPHkk2akJ9/rt2rpzdln4WiKNySns4HDh1i\nn2PHuCY1tcJtFtOLi7kgOZldo6L4SFQUf3d0hq0t5ObKqVqTJuTbb8vQqlsHKYtFzpSnTuWMevXI\nN95wfNawDUQkRfDxxY+z47yODI4M5tX82we23KJcro9Zz/4r+vO+Ofcx9FSozQaUGuVfu7KN3csQ\nQuwB4FvBqU9Ibq2uvRq8DhzAvbVr4yVvbxzo1g3t6te3x9voR0AAsH+/fCxcCMycCTRsCLRpA9Sp\nA6SmAhcuAD17AmPHAlu3AnfdZbTUleJRywNv/d9bGN9lPFadXIXP9n+GF9a/gPae7dH07qYoUUqQ\nmJ0Is8WMIe2GYOOojejRoofRYleJnx/w00/AmTPAvHnAwIGA2Qy0bQvUrw9kZwNnzwLt2wMvvgh8\n/z3QuLHRUleOEALDvLzwTNOm2JqRgUUpKXg1Ph6t69WDT926EACSiouRZjKhf5Mm+Nf99+Pppk1R\nSwijRa+ce+4B5swBpk0DfvgBCAoCkpOBdu2AJk2A/HwgPh7w8gKefx6YMkVe78Q82vJRhE8Kx57E\nPVh6Yik+/u1jeN7tCf/G/vCo5YHU/FQkZieiV8teeKXrKxjZYSTq1a7nENmEHDQ0diLEPgAfkjxS\nwbnHAMwk+XTp648AgOS/K+lLu0Bu3LhxU8MgadPIXq3lrwNRANoJIe4HkAxgNIDAyi629R9w48aN\nGze2U0tLYyHEcCHEZchF3e1CiF2lx1sIIXYAAMkSAG8D2AXgDIB1JGO0ie3GjRs3brSgi9vHjRs3\nbty4Fposfz0RQgwSQsQLIRKEENONlscohBCthBB7hRCxQogYIcR7RstkNEIIDyHEcSHENqNlMRIh\nRGMhxAYhRJwQ4kzpelqNRAgxtfT3cVoIESqEcMwqqRMghFgihEgTQpwud6ypEOK/QohzpX+bVNeP\nUyh/IYQHgHkAngHQAcDLQogOxkplGCUAPiDZAUAvAG/V4M+ijPcgXYY1nbkAdpJ8CEAX1NDPRAjh\nB+BdyPDyTpCJpKONlcqhLIOsmlCe6QB+JdkOwK+lr6vEKZQ/gP8DkEAykaQJwFoAwwyWyRBIppA8\nVvo8D/IH7mesVMYhhGgJYAiARUbLYiRCiEYA+gBYDAAkTSSvGSuVodQGcLcQojaA+gCuGCyPwyC5\nH0DWLYeHAVhe+nw5gOer68dZlL8fgKRyry+jBiu8MoQQ/gC6AYg0VhJD+RbANMj6UTWZ+wGkA1ha\n6gJbJIRoYLRQRkAyGcDXAC4BSAGQQ3K3sVIZjg/JlNLnqQB8qmvgLMrfzS0IIRoC2AjgfZK5Rstj\nBEKIoQDSSB41WhYnoDaA7gDmk+wGoABWTO3vREr92cMgB8QWABoIIcYaK5XzUJbxW911zqL8kwG0\nKve6ZemxGokQog6k4l9NcpPR8hhIbwDPCSEuQroCnxRCrDJWJMO4DOAyybJZ4AbIwaAm0h/ABZLp\nJM0ANgF43GCZjOaqEKI5AJT+TauugbMo/xuJYEKIupCLNz8bLJMhCCEEpF/3DMnZRstjJCQ/ItmS\npD/kPfEbyRpp4ZFMBZAkhHiw9NBTAGINFMlILgHoJYSoX/p7eQo1dPG7HD8DmFD6fAKAakvvOCLD\nt1pIlgghyhLBPAAsqcGJYL0hi9+dEkKcKD32MckdBsrkxjl4B8DqUgMpEcAkg+UxBJKRQogNAI5B\nRscdB/CjsVI5DiFEKIB+ALxKk2xnAPgCwDohRBCAPwCMqrYfd5KXGzdu3NQ8nMXt48aNGzduHIhb\n+btx48ZNDcSt/N24ceOmBuJW/m7cuHFTA3Erfzdu3LipgbiVvxs3btzUQNzK340bN25qIG7l78aN\nGzc1kP8H5MVbQOB0TvcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff683561be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.sin(x[:, np.newaxis] + np.pi * np.arange(0, 2, 0.5))\n",
    "lines = plt.plot(x, y)\n",
    "\n",
    "# lines is a list of plt.Line2D instances\n",
    "plt.legend(lines[:2], ['first', 'second']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I generally find in practice that it is clearer to use the first method, applying labels to the plot elements you'd like to show on the legend:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TfnVJ8PcH/u//7I7P/ejYRxjRZQRGR40WWTDnWdh/Iepb6rE5d3OHx5pMyr48zpCbm4up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\nGzc1kP8H5MVbQOB0TvcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff683579630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y[:, 0], label='first')\n",
    "plt.plot(x, y[:, 1], label='second')\n",
    "plt.plot(x, y[:, 2:])\n",
    "plt.legend(framealpha=1, frameon=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that by default, the legend ignores all elements without a ``label`` attribute set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Legend for Size of Points\n",
    "\n",
    "Sometimes the legend defaults are not sufficient for the given visualization.\n",
    "For example, perhaps you're be using the size of points to mark certain features of the data, and want to create a legend reflecting this.\n",
    "Here is an example where we'll use the size of points to indicate populations of California cities.\n",
    "We'd like a legend that specifies the scale of the sizes of the points, and we'll accomplish this by plotting some labeled data with no entries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sVFRU8Nxzz/G9732PnTt3tp7bWX5fsGTJEs4++2zKysoG/OtmOAdPMxR+F7El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\n8bQDwCNKqQqAiPxT4EvAe4CXgTtr8Jtd1r9DVAZsAH+mlPq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5wv+Na4w9wBeAfygi/4ko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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff677770780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "cities = pd.read_csv('data/california_cities.csv')\n",
    "\n",
    "# Extract the data we're interested in\n",
    "lat, lon = cities['latd'], cities['longd']\n",
    "population, area = cities['population_total'], cities['area_total_km2']\n",
    "\n",
    "# Scatter the points, using size and color but no label\n",
    "plt.scatter(lon, lat, label=None,\n",
    "            c=np.log10(population), cmap='viridis',\n",
    "            s=area, linewidth=0, alpha=0.5)\n",
    "plt.axis(aspect='equal')\n",
    "plt.xlabel('longitude')\n",
    "plt.ylabel('latitude')\n",
    "plt.colorbar(label='log$_{10}$(population)')\n",
    "plt.clim(3, 7)\n",
    "\n",
    "# Here we create a legend:\n",
    "# we'll plot empty lists with the desired size and label\n",
    "for area in [100, 300, 500]:\n",
    "    plt.scatter([], [], c='k', alpha=0.3, s=area,\n",
    "                label=str(area) + ' km$^2$')\n",
    "plt.legend(scatterpoints=1, frameon=False, labelspacing=1, title='City Area')\n",
    "\n",
    "plt.title('California Cities: Area and Population');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The legend will always reference some object that is on the plot, so if we'd like to display a particular shape we need to plot it.\n",
    "In this case, the objects we want (gray circles) are not on the plot, so we fake them by plotting empty lists.\n",
    "Notice too that the legend only lists plot elements that have a label specified.\n",
    "\n",
    "By plotting empty lists, we create labeled plot objects which are picked up by the legend, and now our legend tells us some useful information.\n",
    "This strategy can be useful for creating more sophisticated visualizations.\n",
    "\n",
    "Finally, note that for geographic data like this, it would be clearer if we could show state boundaries or other map-specific elements.\n",
    "For this, an excellent choice of tool is Matplotlib's Basemap addon toolkit, which we'll explore in [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Legends\n",
    "\n",
    "Sometimes when designing a plot you'd like to add multiple legends to the same axes.\n",
    "Unfortunately, Matplotlib does not make this easy: via the standard ``legend`` interface, it is only possible to create a single legend for the entire plot.\n",
    "If you try to create a second legend using ``plt.legend()`` or ``ax.legend()``, it will simply override the first one.\n",
    "We can work around this by creating a new legend artist from scratch, and then using the lower-level ``ax.add_artist()`` method to manually add the second artist to the plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yJ4/T6/X0ySefUMuWLYtEr5GCZGVlUY8ePWxq1K+lzp49S/379ydJkuj06dNq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\n888/KzK3uCRJqo8ZiYqKovP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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff675a0ac18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "lines = []\n",
    "styles = ['-', '--', '-.', ':']\n",
    "x = np.linspace(0, 10, 1000)\n",
    "\n",
    "for i in range(4):\n",
    "    lines += ax.plot(x, np.sin(x - i * np.pi / 2),\n",
    "                     styles[i], color='black')\n",
    "ax.axis('equal')\n",
    "\n",
    "# specify the lines and labels of the first legend\n",
    "ax.legend(lines[:2], ['line A', 'line B'],\n",
    "          loc='upper right', frameon=False)\n",
    "\n",
    "# Create the second legend and add the artist manually.\n",
    "from matplotlib.legend import Legend\n",
    "leg = Legend(ax, lines[2:], ['line C', 'line D'],\n",
    "             loc='lower right', frameon=False)\n",
    "ax.add_artist(leg);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a peek into the low-level artist objects that comprise any Matplotlib plot.\n",
    "If you examine the source code of ``ax.legend()`` (recall that you can do this with within the IPython notebook using ``ax.legend??``) you'll see that the function simply consists of some logic to create a suitable ``Legend`` artist, which is then saved in the ``legend_`` attribute and added to the figure when the plot is drawn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) | [Contents](Index.ipynb) | [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) >"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
